AI Breakdown
The podcast where we use AI to breakdown the recent AI papers and provide simplified explanations of intricate AI topics for educational purposes.
The content presented here is generated automatically by utilizing LLM and text to speech technologies. While every effort is made to ensure accuracy, any potential misrepresentations or inaccuracies are unintentional due to evolving technology. We value your feedback to enhance our podcast and provide you with the best possible learning experience.
If you see a paper that you want us to cover or you have any feedback, please reach out to us on twitter https://twitter.com/agi_breakdown
Latest Episode
Mathematical exploration and discovery at scale (15.11.2025)
Previous Episodes
- Kosmos: An AI Scientist for Autonomous Discovery
- World Simulation with Video Foundation Models for Physical AI
- Towards Robust Mathematical Reasoning
- ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models
- Roboflow100-VL: A Multi-Domain Object Detection Benchmark for Vision-Language Models
- ImpossibleBench: Measuring LLMs’ Propensity of Exploiting Test Cases
- Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset
- Reasoning with Sampling: Your Base Model is Smarter Than You Think
- DeepSeek-OCR: Contexts Optical Compression
- The Markovian Thinker
- DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL
- Towards a Physics Foundation Model
- Scalable Option Learning in High-Throughput Environments
- Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
- Reverse-Engineered Reasoning for Open-Ended Generation
- Scaling Performance of Large Language Model Pretraining
- General Social Agents
- We need a new ethics for a world of AI agents
- Hierarchical Reasoning Model
- ARC-Hunyuan-Video-7B: Structured Video Comprehension of Real-World Shorts
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